The cornerstone of an effective energy conservation program is the ability of the individual consumer to get a clear signal of the results of their energy conservation efforts and investments. For the vast majority of consumers, the only real measuring tool that signals the effect of their conservation efforts is their monthly utility bill. Their bill does not provide a clear signal due to changes in the weather and volatility in energy prices. Without clear feedback, consumers become less interested in attempting to control their energy usage, believing they have no control over their energy bill.
Only the largest consumers have been able to get a true understanding of the benefits of their conservation efforts through labor-intensive energy audits performed on a manual basis. Because of the high cost of these individual audits, it is not cost effective to perform them for retail consumers such as residential or small- to medium-sized commercial customers. The high cost of individual audits is driven by the need to manually process usage and weather data, individually deal with data deficiencies and to make manual adjustments for incomplete or inaccurate information. In manual audits, model selection occurs at the discretion of a human auditor, although there have been some attempts at automated model generation, such as the Prism approach, described in Fels, M., “PRISM: An Introduction”, Energy and Buildings, 9 (1986), pp. 5–18.
Utilities may develop a prediction of a consumer's usage at “normal” weather. Typically they do so by developing a linear fit between usage and weather and applying that fitted model to normalized weather. Those equations could be used in theory to calculate individual changes in energy efficiency. However, the accuracy of this method is not sufficient for these calculations. The Prism approach attempts to overcome this deficiency by the inclusion of a household specific variable tau. However, the Prism model effectively forces all households into the same equation structure of a linear regression. Prism also calculates a normal annual consumption in its determination of efficiency, and does not use the current weather condition to determine efficiency at that weather condition. The Prism approach develops a baseline and a non-baseline model for each consumer and exercises both models on normalized weather. The Prism approach is thus subject to numerous shortcomings including model inaccuracy far exceeding the change in normal consumption and errors caused by non-constant period lengths that can obscure the changes in efficiency.
Therefore, a system and method of determining the overall efficiency of a heating system and a cooling system for a building that overcomes the above listed shortcomings is needed.